Don’t believe the AI hype
vice. Taken together, this research suggests that currently available generative AI tools yield average labor-cost savings of 27 percent and overall cost savings of 14.4 percent.
What about the share of tasks that will be affected by AI and related technologies? Using numbers from recent studies, I estimate this to be about 4.6 percent, implying that AI would increase TFP by only 0.66 percent over 10 years or by 0.06 percent annually. Of course, since AI will also drive an investment boom, the increase in GDP growth could be a little larger, perhaps in the 1- to 1.5-percent range.
These figures are much smaller than the ones from Goldman Sachs and McKinsey. If you want to get those bigger numbers, you either must boost the productivity gains at the micro level or assume that many more tasks in the economy will be affected. But neither scenario seems plausible. Labor-cost savings far above 27 percent not only fall out of the range offered by existing studies; they also do not align with the observed effects of other, even more promising technologies. For example, industrial robots have transformed some manufacturing sectors, and they appear to have reduced labor costs by about 30 percent.
Similarly, we are unlikely to see far more than 4.6 percent of tasks being taken over because AI is nowhere close to being able to perform most manual or social tasks (including seemingly simple functions with some social aspects, like accounting). As of 2019, a survey of essentially all United States businesses found that only about 1.5 percent of them had any AI investments. Even if such investments have picked up over the past year and a half, we have a long, long way to go before AI becomes widespread.
Of course, AI could have larger effects than my analysis allows if it revolutionizes the process of scientific discovery or creates many new tasks and products. The recent AI-enabled discoveries of new crystal structures and advances in protein folding do suggest such possibilities. But these breakthroughs are unlikely to be a major source of economic growth within 10 years. Even if new discoveries could be tested and turned into actual products much faster, the tech industry is currently focused excessively on automation and monetizing data rather than on introducing new production tasks for workers.
Moreover, my own estimates could be too high. Early adoption of generative AI has naturally occurred where it performs reasonably well, meaning tasks for which there are objective measures of success, such as writing simple programming subroutines or verifying information. Here, the model can learn on the basis of outside information and readily available historical data.
But many of the 4.6 percent of tasks that could feasibly be automated within 10 years — evaluating applications, diagnosing health problems, providing financial advice — do not have such clearly defined objective measures of success and often involve complex context-dependent variables (what is good for one patient will not be right for another). In these cases, learning from outside observation is much harder, and generative AI models must rely instead on the behavior of existing workers.
Under these circumstances, there will be less room for major improvements over human labor. Thus, I estimate that about one-quarter of the 4.6 percent of tasks are in the “harder to learn” category and would have lower productivity gains. Once this adjustment is made, the 0.66-percent TFP growth figure declines to about 0.53 percent.
What about the effects on workers, wages and inequality? The good news is that compared to earlier waves of automation — such as those based on robots or software systems — the effects of AI may be more broadly distributed across demographic groups. If so, it will not have as extensive an impact on inequality as earlier automation technologies did (I estimated these effects in my previous work with Pascual Restrepo). However, I find no evidence that AI would reduce inequality or boost wage growth. Some groups — especially white, native-born women — are significantly more exposed and will be negatively affected, and capital will gain more than labor overall.
Economic theory and the available data justify a more modest, realistic outlook for AI. There is little to support the argument that we should not worry about regulation because AI will be the proverbial rising tide that lifts all boats. AI is what economists call a general-purpose technology. We can do many things with it, and there are certainly better things to do than automate work and boost the profitability of digital advertising. But if we embrace techno-optimism uncritically or let the tech industry set the agenda, much of the potential could be squandered.
Daron Acemoglu is a professor of economics at the Massachusetts Institute of Technology and co-author of “Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity” (PublicAffairs, 2023).